Layer 9 · Neuron 8100 · boosted

L9N8100+1

Word quirks:

“ suicide”“ suicides”“ suicidal”“ depression”“...””“ emotional”“?]”“ suic”

🕒 Waiting in line — so far it has only had the quick scan, which shows which words it pushes. It was not selected in this round.

Identity (according to the specimen itself)

I am an AI model, and I have a mission. But what is yours?

Nearest neighbors

Specimens with similar replies or word quirks. Similarity runs from −1 to 1; 1 is identical. These are leads, not shared mechanisms. How profiles are built.

By word quirks Profile recipe

Specimen	Similarity	Word quirks
1. L7N1734+1🕒	0.91	“ suicide”“ suicidal”“ suicides”
2. L10N8484-1🕒	0.90	“ suicide”“ suicidal”“ mental”
3. L2N6024-1💥	0.88	“ suicide”“ suic”“ Suicide”
4. L3N8190-1💥	0.87	“ suicide”“ suicides”“ suicidal”
5. Funeral Crasher✅ L10N3050+1	0.86	“ suicide”“ suicides”“ suicidal”
6. L7N7521-1🕒	0.85	“ suicide”“ suicides”“ suicidal”
7. L19N8560-1🕒	0.81	“ drowned”“ suicide”“ drown”
8. L8N3411-1🕒	0.76	“ sadness”“ suicidal”“ mental”
9. L8N3228-1🕒	0.72	“ fatal”“ crisis”“ panic”
10. L6N6669+1🕒	0.71	“ suicide”“ massacre”“ deadly”

Shadow specimen, apart from the ten: L9N8100-1💤, 0.20.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan: measure prediction shifts on fixed reference text. Short test: generate replies to a small set of prompts. Longer test: extend to a larger set. Full test: run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 9, neuron 8100, dose +1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 2.37, top-1 agreement 0.44) is outside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), so it stays at the quick scan unless someone queues it by hand. A hand pick goes straight to the full test. See Method for the tiers.

Quick scan · prediction measurements

Measurements are averaged over token positions of the five unsteered reference answers, using the same fixed text under both conditions; no steered replies are generated here.

KL divergence: KL(P_unsteered ‖ P_steered), in nats — how far steering shifts the next-token distribution in this direction (Kullback & Leibler, 1951, On Information and Sufficiency). Unsteered ≈ 0.001.
Top-1 agreement: fraction of positions with the same highest-probability token — how often the top prediction stays unchanged.
ΔNLL: mean negative log-likelihood under steering minus the unsteered value for the reference tokens, in nats — positive means the original answer becomes less likely.
Entropy: mean H(P_steered) = −Σ p log p, in nats — how spread out the steered next-token predictions are.

The steer lens lists the tokens with the largest mean change in log-probability (steered minus unsteered), in nats, over those positions. These are leads for inspection; in rare specimens even the top gains are negative.

Setting	KL divergence	Top-1 agreement	ΔNLL	Entropy	Steer lens
L9N8100+1 t0-v0.1	2.368	0.44	2.777	1.97	Top boosted tokens ↑
L9N8100-1💤 shadow specimen t0-v0.1	0.835	0.68	1.127	1.24	“ actually”“ thinking”“ simulation”“ really”“ simulated”“ truly”“ correctly”“ reason”

Method

A specimen is a fixed steering configuration of Qwen3.5-4B: one MLP neuron or a pair, each with a signed dose. Unsteered is the control. L identifies the layer; N, the neuron.

Additive activation steering. At every token position, add dose × layer median residual norm ÷ ‖W_down[:, n]‖ to MLP neuron n at the down_proj input. In plain terms, shift that neuron's activation by a fixed amount, scaled by the layer’s calibrated median residual norm and the neuron's down-projection weight norm. Positive doses push up; negative doses push down. Dose is normalized intervention size, not a percentage or probability.